System and method for event-based vehicle operation

ABSTRACT

Embodiments of a method and/or system for facilitating event-based vehicle operation can include determining a vehicle route; determining geographic regions for the vehicle route; monitoring the determined geographic regions for events; determining an event of interest from the detected events; and/or dynamically facilitating modification of vehicular operation of the vehicle based on the event of interest, such as in response to determination of the event of interest.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/420,414, filed 23 May 2019. That application is a continuation ofU.S. application Ser. No. 15/985,491 filed 21 May 2018, which is acontinuation-in-part of U.S. application Ser. No. 15/902,935 filed 22Feb. 2018, which is a continuation of U.S. application Ser. No.15/250,735, filed 29 Aug. 2016, which is a continuation-in-part of U.S.application Ser. No. 14/882,318 filed 13 Oct. 2015, which claims thepriority of U.S. Provisional Application No. 62/063,078 filed 13 Oct.2014 and is a continuation-in-part of U.S. application Ser. No.14/643,958 filed 10 Mar. 2015. Application Ser. No. 14/643,958 is acontinuation-in-part of U.S. application Ser. No. 14/574,966, filed 18Dec. 2014, which claims the benefit of U.S. Provisional Application No.61/918,126, filed 19 Dec. 2013, U.S. Provisional Application No.62/060,407, filed 6 Oct. 2014, and U.S. Provisional Application No.62/006,632, filed 2 Jun. 2014. Application Ser. No. 14/643,958 is also acontinuation-in-part of application Ser. No. 14/501,436 filed 30 Sep.2014, which is a continuation-in-part of application Ser. No.14/043,479, filed 1 Oct. 2013, which claims the benefit of U.S.Provisional Application No. 61/709,103, filed 2 Oct. 2012, U.S.Provisional Application No. 61/782,687, filed 14 Mar. 2013, and U.S.Provisional Application No. 61/784,809, filed 14 Mar. 2013. ApplicationSer. No. 14/501,436 also claims the benefit of U.S. ProvisionalApplication No. 61/885,322, filed 1 Oct. 2013, U.S. ProvisionalApplication No. 61/918,126, filed 19 Dec. 2013, and U.S. ProvisionalApplication No. 62/006,632, filed 2 Jun. 2014.

U.S. application Ser. No. 15/985,491 additionally claims the benefit ofU.S. Provisional Application No. 62/508,888 filed 19 May 2017.

All of the aforementioned applications are incorporated herein in theirentireties by this reference.

TECHNICAL FIELD

This invention relates generally to the vehicle routing field, and morespecifically to a new and useful system and method in the vehiclerouting field.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1A-1B are schematic representations of embodiments of the method.

FIG. 2 is a schematic representation of an embodiment of the system.

FIG. 3 is a schematic representation of a variation of the routeparameters.

FIG. 4 is a schematic representation of a first variation of determiningthe geographic regions.

FIG. 5 is a schematic representation of a second variation ofdetermining the geographic regions, where the geographic regions includea set of geographic locations.

FIG. 6 is a schematic representation of a third variation of determiningthe geographic regions.

FIG. 7 is a schematic representation of a first variation of dynamicallydetermining the geographic regions. as the vehicle moves.

FIG. 8 is a schematic representation of a second variation ofdynamically determining the geographic regions as the vehicle moves.

FIG. 9 is a schematic representation of a fourth variation ofdetermining the geographic region.

FIGS. 10A-10B are schematic representations of variations of the methodassociated with aggregate regions.

FIG. 11 is a schematic representation of a first variation of monitoringthe geographic regions for an event, including monitoring a set ofgeographic locations, detecting an event in a geographic location,determining that the geographic location is within the geographic regionfor a vehicle, and notifying the vehicle and/or vehicle entity of theevent.

FIG. 12 is a schematic representation of a second variation ofmonitoring the geographic regions for an event, including detectingevents within a set of geographic locations, determining that thegeographic location is within the geographic region for a vehicle,determining that the event is of interest to the vehicle and/or vehicleentity, and notifying the vehicle and/or vehicle entity of the event.

FIG. 13 is a schematic representation of a second variation ofmonitoring the geographic region for an event, including monitoring anaggregate geographic region, detecting an event within the geographicregion, determining the event location, determining a vehicle associatedwith the event location for the event timeframe, notifying the vehicleand/or vehicle entity of the event, and optionally determining a newroute for the vehicle and aggregate monitoring region.

FIG. 14 is a schematic representation of an example of vehicle classmodule selection.

FIG. 15 is a schematic representation of an example of an interface fordetermining the route parameters.

FIG. 16 is a schematic representation of an example of receiving avehicle interaction region.

FIG. 17 is a schematic representation of an example of receiving avehicle route.

FIG. 18 is a schematic representation of an example of receiving a setof waypoints and automatically determining the vehicle route based onthe waypoints.

FIG. 19 is a schematic representation of an example of receiving ananticipated travel time.

FIG. 20 is a schematic representation of an example of presentingnotifications for events proximal the vehicle route.

FIG. 21 is a schematic representation of an example of presentingnotifications for events proximal vehicle routes for multiple vehicles.of presenting notifications for events proximal the vehicle route.

FIG. 22 is a schematic representation of a second example of presentingevent notifications.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

1. Overview.

As shown in FIGS. 1A-1B, an embodiment of a method 100 for facilitatingevent-based vehicle operation (e.g., facilitating improved event-basedvehicle routing for a vehicle or set of vehicles, etc.) can include:determining a vehicle route S100; determining geographic regions for thevehicle route S200; monitoring the determined geographic regions forevents S300; determining an event of interest from the detected eventsS400; and/or dynamically facilitating modification of vehicularoperation of the vehicle based on the event of interest S500 (e.g.,controlling vehicle operation to travel along an adjusted vehicle route;determining and presenting a notification for the vehicle and/or vehicleentity; etc.), such as in response to event detection within theidentified geographic region. Embodiments of the method 100 and/orsystem 200 can function to facilitate real- or near-real time pathplanning for vehicles and/or vehicle entities. Path planning, othermeans of dynamically facilitating vehicular operation modification,event detection, and/or other suitable portions of the method 100 and/orsystem 200 can be performed in real- or near real-time, where real- ornear real-time can include any one or more of: concurrently withcollecting external signals (e.g., detecting an event and/orfacilitating vehicular operation modification based on an event assignals corresponding to the event are collected; etc.); live inrelation to an event (e.g., as an event is occurring, or before anevent, etc.), external signal for an event, vehicular operation (e.g.,during vehicular travel on a vehicle route; prior to arrival at ageographic location proximal the event; etc.), and/or other suitablecomponent; satisfying a threshold time period (e.g., from the initiationof an event; from the first collected external signal for an event;etc.), and/or other suitable variants.

In a first example, the method 100 can include, at a processing system(e.g., remote from a set of vehicles; including processors of the set ofvehicles; first party processing systems; third party processingsystems; user processing systems; etc.): determining vehicle routes forthe set of vehicles (e.g., based on selections of waypoints received bya vehicle entity at a vehicle entity interface, etc.); determining a setof geographic regions for the set of vehicles based on the vehicleroutes (e.g., determining geographic regions that include the vehicleroutes, the geographic regions to be monitored for events relevant tothe set of vehicles; etc.); collecting external signals for the set ofgeographic regions (e.g., collecting signals from sources on theInternet, such as social networking sources, etc.); detecting a set ofevents associated with the set of geographic regions based on theexternal signals (e.g., detecting an event in response to Internet-basedsignals satisfying a threshold condition); for each vehicle of the setof vehicles: filtering the set of events for an event of interest forthe vehicle based on a vehicle parameter associated with the vehicle(e.g., identifying events likely to affect a vehicle of the set ofvehicles if traveling along the corresponding vehicle route; etc.); anddynamically facilitating modification of vehicular operation of thevehicle (e.g., to travel along an adjusted vehicle route distinct fromthe vehicle route corresponding to the vehicle, etc.), based on theevent of interest for the vehicle (e.g., determining and providing anadjusted vehicle route that avoids the one or more geographic regionscorresponding to the event of interest, etc.).

In a second example, the method 100 can include, determining a set ofgeographic regions based on a vehicle route for the vehicle; at aprocessing system (e.g., remote from the vehicle), during a time periodof vehicular operation of the vehicle (e.g., along the vehicle route,during vehicle deployment preparation prior to travel along the vehicleroute, during charging, etc.): collecting external signals for the setof geographic regions; detecting a set of events associated with the setof geographic regions based on the external signals; determining eventparameters for the set of events, the event parameters describing theevent (e.g., event category, event content, associated confidencelevels, etc.); determining an event of interest from the set of eventsbased on the event parameters (e.g., an event category matching an eventcategory condition associated with a vehicle class of a vehicle); anddynamically facilitating modification of vehicular operation of thevehicle (e.g., to travel along an adjusted vehicle route distinct fromthe vehicle route corresponding to the vehicle, etc.), based on theevent of interest.

All or part of the method 100 can be performed by any combination ofcomponents of the system 200 described herein, a set of remoteprocessing systems, vehicles, and/or any other suitable systemcomponents.

2. Benefits

The inventors have discovered that there is at least a need for vehiclesto be dynamically routed based on event locations. For example, vehiclesmay wish to avoid event locations that could have adverse effects onvehicle operation (e.g., UAVs may wish to avoid areas with fire, toavoid heat damage or smoke screening; terrestrial vehicles may wish toavoid areas with roadblocks; vehicles may wish to avoid locationsassociated with natural disasters; etc.), and treat event locations asdynamic no-fly zones. In another example, vehicles may wish to encounterevent locations (e.g., news helicopters may wish to travel close to asmany events as possible to maximize the value of scrambling the vehicle;vehicle entities controlling drones may wish to capture events ofinterests with on-board optical sensors; etc.), where the eventlocations can be treated as dynamic waypoints.

A difficulty with this dynamic routing, however, is that events canoccur spontaneously. The nature of such events can make route predictionand planning a challenge, since an adverse event can occur along thevehicle route while the vehicle is already en route (e.g.,weather-related adverse events, traffic-related adverse events, etc.).Furthermore, events can be difficult to predict, and can be difficult todetect with sufficient speed, certainty, and/or fidelity to beactionable by a vehicle (and/or associated vehicle entity such as apilot of the vehicle etc.). For example, a drone operator (e.g., UAVoperator) needs to know, in near-real time, that a fire just occurred inthe drone's immediate flight path, just outside of the drone's sensingrange. Moreover, events can be transient—an event in the vehicle routethat is currently on-going may have ended by the time the vehiclearrives at the event location (e.g., such that re-routing of the vehicleis unnecessary).

Additionally, because the vehicle is a moving asset that can besensitive to not only events in the immediate route but also to eventsin surrounding areas, detecting events associated with the vehicle canrequire the system 200 to monitor a large number of different spaces,each of which can be relevant at different times. Furthermore, theidentity and/or number of monitored spaces can change over time (e.g.,due to expected or unexpected route or vehicle operation changes). Thiscan require a substantial amount of processing power and memory for justa single vehicle; when embodiments of the method 100 and/or system 200are applied to multiple vehicles (e.g., within a fleet or acrossmultiple entities), the processing power increases drastically, and theprocessing speed drops accordingly. These computing challenges can befurther compounded because different vehicles and/or vehicle entitiescan be interested in different events (e.g., depending on vehicle class,vehicle entity preferences, etc.); in examples, each vehicle's eventfilter is dynamically reassigned to the geographic regions correspondingto the current vehicle's location whenever the vehicle moves, in orderto account for vehicle travel and the different events of interestacross vehicles, vehicle entities, and/or other parameters.

Thus, there is a need in the vehicle routing field to create a new anduseful system 200 and method 100, such as for event-based vehiclerouting. This invention provides such new and useful system 200 andmethod 100.

First, embodiments of the system 200 and method 100 can detect past,present, and/or future events with high fidelity and confidence in real-or near-real time (e.g., based on social networking system posts,emergency systems notifications, en-route vehicles' sensor streams,etc.), and can dynamically facilitate modification of vehicularoperation (e.g., through vehicle re-routing, providing relevantroute-related and/or event-related notifications to the vehicle and/orvehicle entity), such as in real- or near-real time. As such, thetechnology can improve the technical fields of at least vehicle routing,fleet management, event detection and/or characterization, and/or anyother relevant fields. Embodiments of the method 100 and/or system 200can additionally or alternatively: automatically determine a new routefor the vehicle (e.g., to avoid events or treat events as waypoints),monitor raw signals (e.g., social networking system posts) for contentabout the vehicle (e.g., allowing the vehicle entity to gauge publicresponse to the vehicle), provide information about the event in realtime, near-real time, or asynchronously to the vehicle entity oroperator, and/or perform any other suitable functionality.

Second, embodiments of the method 100 and/or system 200 can additionallyor alternatively transform entities into different states or things. Forexample, embodiments can facilitate dynamic modification of aspects ofvehicular operation, including at least one of: movement (e.g.,facilitating re-routing of the vehicle to avoid or attend events ofinterest; etc.), durability (e.g., by routing the vehicle to avoidevents that may adversely affect the vehicle; etc.), data collection(e.g., facilitating control sensor data sampling to collect datafacilitating analysis by the event detection system, event monitoringsystem, and/or other suitable components; etc.), battery life (e.g.,through route optimization, etc.), and/or any other suitable vehicularoperation aspects. As such, embodiments can leverage system componentoutputs (e.g., detected events, event parameters, notifications, etc.)to facilitate physical actions performed by a vehicle and/or othersuitable system component.

Third, embodiments of the method 100 and/or system 200 can additionallyor alternatively confer improvements in the functioning of a processingsystem (e.g., computing system, remote processing system, vehicleprocessing system, etc.) itself. In variations, the system 200 andmethod 100 can aggregate the geographic regions (e.g., geographicregions) across multiple vehicles (e.g., within the same or differentfleets) into a single, larger, aggregate region (e.g., aggregategeographic region), thereby reducing the amount of processing powerand/or memory required to constantly monitor the vehicle routes and/oridentify relevant events (e.g., enabling the system 200 to scale withthe number of inputs, vehicles, and/or other parameter; by reducingredundant computing processes; etc.). Additionally or alternatively, anyportions of the method 100 can be adapted to a plurality of vehicles forimproving computational efficiency of associated processing systems.

Fourth, the technology can provide technical solutions necessarilyrooted in computer technology (e.g., leveraging Internet-based sourcesfor external signals informing event detection and/or characterizationsuch as for identifying events of interest to particular vehicles;generating and/or applying computational modules such as event filtermodules for delivering vehicle-specific and/or vehicle entity-specificcontent to a plurality of vehicles and/or vehicle entities, etc.) suchas to overcome issues specifically arising with computer technology(e.g., achieving centralized, remote, event-based management of a fleetof vehicles; achieving dynamic real-time or near real-time event-basedrouting for vehicles; etc.).

Fifth, the technology can amount to an inventive distribution offunctionality across a network including a remote processing system,vehicles, vehicle entities, and/or other suitable components. In anexample, embodiments of the method 100 can be performed in a centralizedmanner by a remote processing system (e.g., including a deploymentsystem, event detection system, event monitoring system, other systemcomponents, etc.), in order to enable improved event-based fleetmanagement of a plurality of vehicles such as through communicatingrelevant information (e.g., event-related information, route-relatedinformation) to vehicles and/or vehicle entities.

However, in specific examples, the technology can provide any othersuitable improvements, such as in the context of using non-generalizedprocessing systems and/or other suitable components, through performingsuitable portions of the method 100 and/or applying suitable componentsof the system 200.

3. System.

As shown in FIG. 2, embodiments of a system 200 (e.g., a vehicleoperation system) for facilitating improved event-based vehicleoperation can include: a vehicle 210, a deployment system 220 (e.g., avehicle deployment system), an event system 230 (e.g., an eventdetection system), and/or a monitoring system 240 (e.g., eventmonitoring system).

The system 200 and/or portions of the system 200 can entirely orpartially be executed by, hosted on, communicate with, and/or otherwiseinclude: a remote processing system (e.g., a server, at least onenetworked processing system, stateless, stateful; etc.), a localprocessing system, vehicles 210, an original equipment manufacturer(OEM) system (e.g., an OEM platform hosted by a OEM server, etc.),social network systems, a user device (e.g., a vehicle entity device,mobile phone device, other mobile device, personal computing device,tablet, wearable, head-mounted wearable computing device, wrist-mountedwearable computing device, etc.), databases (e.g., for storing anysuitable data and/or data types described herein, such as withassociations between any suitable data and/or data types describedherein, etc.), application programming interfaces (APIs) (e.g., foraccessing data described herein, etc.) and/or any suitable component.Communication by and/or between any components of the system 200 caninclude wireless communication (e.g., WiFi, Bluetooth, radiofrequency,Zigbee, Z-wave, etc.), wired communication, and/or any other suitabletypes of communication.

The components of the system 200 can be physically and/or logicallyintegrated in any manner (e.g., with any suitable distributions offunctionality across the components, such as in relation to portions ofthe method 100; where a remote processing system includes the deploymentsystem, event system, and/or monitoring system; etc.). Additionally oralternatively, components of the system 200 can be integrated with anysuitable existing components (e.g., third party APIs, platforms,systems, vehicles, vehicle interfaces, etc.).

Additionally or alternatively, components of the system 200 can includeany suitable components described in and/or be configured in anysuitable manner described in U.S. application Ser. Nos. 14/643,958 and15/250,735, which are each incorporated in their entireties by thisreference. However, the system 200 can include any other suitablesub-system or combination thereof, and components of the system 200 canbe configured in any suitable manner.

The vehicle 210 can function to physically traverse along a vehicleroute. Additionally or alternatively, the vehicle 210 can function tosample vehicle sensor data, communicate with other system components(e.g., a remote processing system, etc.), execute control instructions,and/or perform other suitable processes. The vehicle 210 can be anaerial vehicle (e.g., drone, airplane, helicopter), terrestrial vehicle(car, truck, autonomous vehicle), aquatic vehicle (e.g., ship,submarine, etc.), and/or any other suitable vehicle. The vehicle 210 canbe manned, unmanned (e.g., remote controlled, automatic, semi-automatic,etc.), and/or otherwise controlled. The vehicle is preferably part offleet and/or associated with vehicle entity (e.g., the fleet manager,vehicle operator, etc.), but can be otherwise associated with any othersuitable set of secondary vehicles. The vehicle 210 can includelocomotion mechanisms (e.g., rotors, motor, etc.), communicationmechanisms (e.g., WiFi, BLE, cellular, etc.), vehicle sensors (e.g.,on-board sensors, etc.), processing systems (e.g., CPU, GPU), and/or anyother suitable component. Vehicle sensors can include any one or moreof: optical sensors (e.g., cameras; in-vehicle cameras; exteriorcameras; dashboard cameras; infrared cameras; 3D stereo cameras;monocular camera front-view cameras; side-view cameras; etc.), proximitysensors (e.g., radar, electromagnetic sensor, ultrasonic sensor, lightdetection and ranging, light amplification for detection and ranging,line laser scanner, laser detection and ranging, airborne laser swathmapping, laser altimetry, sonar, etc.), movement sensors (e.g.,position, velocity, and/or acceleration sensors; accelerometers;gyroscopes; etc.), location sensors (e.g., GPS sensors, compass data,etc.), odometer, altimeter, environmental sensors (e.g., pressure,temperature, etc.), light sensors (e.g., infrared sensors, ambient lightsensors, etc.), fuel sensors (e.g., percentile-based, distance-based,etc.), oxygen sensors, throttle position, gear sensor (e.g., drive,neutral, park, reverse, gear number, etc.), HVAC sensors (e.g., currenttemperature, target temperature, etc.), internal monitoring sensors(e.g., battery monitoring systems, voltage sensors, etc.), and/or anyother vehicle sensors.

Vehicular operation corresponding with one or more vehicles 210 can beassociated with any one or more of: movement (e.g., along a vehicleroute; movement in relation to geographic regions, event locations,other locations; location of the vehicle 210; etc.), durability (e.g.,lifespan, vehicle component status, maintenance-related statuses,vehicle damager characteristics such as damage location, mechanicaldamage characteristics, electrical damage characteristics, etc.), datacollection (e.g., by vehicle sensors), battery (e.g., battery life,efficiency, etc.), processing (e.g., by a vehicle processing system;processing of control instructions; responding to communications fromcomponents of the system; etc.), vehicle interaction (e.g., vehicleinteraction regions, etc.), and/or any other suitable aspects ofvehicular operation.

Vehicle parameters preferably describe a vehicle 210 and/or associatedvehicular operation. Vehicle parameters can include any one or more of:vehicle identifiers, vehicle class (e.g., vehicle type; aerial;terrestrial; aquatic; make; model; age; engine type; battery type;brakes type; fuel type; associated sensor types; position, velocity,and/or acceleration parameters; size parameters such as dimensionparameters and/or weight parameters; etc.), vehicle operationparameters, locomotor mechanism parameters, vehicle sensor data,emissions parameters, and/or any other suitable parameters. Vehicleparameters can be user determined, automatically determined (e.g., by asystem component), customer-determined, and/or otherwise determined atany suitable time and frequency (e.g., for storage in a database of thesystem). Vehicle sensor data sampled and/or otherwise collected byvehicles 210, and/or other suitable vehicle parameters can be used byany suitable system component for identifying events of interest (e.g.,based on analyzing vehicle sensor data in combination with analyzingevent parameters; etc.), detecting events, generating notifications,and/or performing any other suitable portion of the method 100.

The vehicle route can be pre-determined (e.g., before vehicledeployment, before vehicle movement, etc.), dynamically determined(e.g., while the vehicle 210 is en route; during vehicle operation;etc.), and/or otherwise determined (e.g., a first vehicle routepre-determined prior to vehicle deployment, a second vehicle routeupdated from the first vehicle route during vehicular operation oftravel along the first vehicle route; etc.). All or a portion (e.g., thenext 10 minutes) of the overall vehicle route (e.g., from deployment toreturn) and/or segments of the vehicle route can be determined each timethe route is determined, and/or determined at any suitable frequency andtime. The vehicle route can be determined by a user (e.g., entered by avehicle entity at a vehicle entity interface, as shown in FIGS. 17-18),automatically determined (e.g., by the deployment system 220, eventsystem 230, monitoring system 240, remote processing system, etc.),vehicle 210 (e.g., communicated to a remote processing system byself-controlled vehicles to facilitate event-based route modifications,which can be communicated back to the self-controlled vehicles, etc.),and/or otherwise determined. The vehicle route can be determined basedon route parameters and/or otherwise determined. Route parameters can beuser determined, automatically determined, customer-determined, and/orotherwise determined. Route parameters can include one or more:waypoints, geographic areas (e.g., geofences, etc.), series ofgeographic locations cooperatively forming a route, directions, times(e.g., waypoint arrival times, trip beginning, trip end, etc.), vehicleoperation parameters (e.g., vehicle speed, vehicle acceleration),no-entry zones (e.g., no-fly zones), events (e.g., anticipated, current,past, etc.), geographic regions (e.g., corresponding to events ofinterest), events (e.g., selected by a vehicle entity to avoid), and/orany other suitable parameter.

Route parameters can optionally include a vehicle interaction region.The vehicle interaction region can define the region to be monitored forevents (e.g., geographic regions for the vehicle route), the region thatthe vehicle affects (e.g., through backwash, region of uncertainty,region of potential movement, etc.), the region that can affect vehicleoperation (e.g., where obstacles and/or forces within the region canchange vehicle operation), and/or define any other suitable region. Thevehicle interaction region preferably surrounds the vehicle 210, but canalternatively be distal the vehicle 210 (e.g., be a toroid centeredabout the vehicle 210) and/or otherwise related to the vehicle 210. Inan example, the vehicle interaction region can be associated withenvironmental interaction for vehicles 210 within a vehicle class (e.g.,describing how vehicles 210 within the vehicle class interact with thesurrounding environment, such as in relation to vehicle components, suchas locomotor mechanisms and/or vehicle sensors corresponding to thevehicle class, etc.). The vehicle interaction region can be a radiusfrom the vehicle 210, a geofence associated with the vehicle 210 (e.g.,about the vehicle 210), a volume proximal the vehicle 210 (e.g.,surrounding the vehicle 210), any suitable dimensions (e.g., relating togeometry, time, sensors, motion, etc.) and/or be otherwise defined. Thevehicle interaction region can be determined by a user (e.g., thevehicle entity), by the vehicle 210 (e.g., based on obstacles detectedby on-board sensors, vehicle sensor data, etc.), by the deploymentsystem 220 and/or another remote processing system (e.g., based onvehicle class, historical operation parameters, etc.), by vehicleparameters (e.g., vehicle footprint or dimensions, vehicle sensor data,vehicle class, other vehicle parameters), and/or otherwise determined.The vehicle interaction region can be the same (e.g., in size,dimension, area, etc.) for the entire route, vary along the vehicleroute, and/or otherwise related to the vehicle route. The vehicleinteraction region can be predetermined for each location along thevehicle route, be dynamically determined for each location while thevehicle 210 is en route, and/or be otherwise determined. The vehicleinteraction region can be determined (e.g., automatically; dynamically)based on the vehicle class (e.g., terrestrial or aerial; large or small;lift mechanism type, etc.); vehicle operation parameters (e.g., currentor future parameters), such as vehicle motion, such as acceleration orvelocity; other vehicle parameters; event parameters (e.g., class, type,intensity, past duration, anticipated duration, number of proximalevents, distance from the anticipated vehicle location; surroundingevent parameters; etc.); analysis of on-board vehicle sensor streams(e.g., increased in response to detected in-path obstacles, re-drawn toexclude in-path obstacles, etc.); and/or be otherwise determined.However, the vehicle 210, vehicular operation, vehicle parameters,routes, and/or other associated components can be configured in anysuitable manner.

The deployment system 220 can function to control operation of one ormore vehicles 210. The deployment system 220 can monitor deployedvehicles (e.g., vehicles en-route for a vehicle route; receive telemetrydata such as vehicle sensor data and/or route-related communicationsfrom the deployed vehicles; monitor vehicle statuses; etc.); receiveevent information from the event system 230; determine vehicle controlinstructions (e.g., for deployment and/or subsequent vehicularoperation, etc.), such as the route parameters (e.g., for travelingalong a determined vehicle route, etc.) and/or vehicle operationparameters (e.g., received from an operator, automatically generatedbased on the events, for operating vehicle components, etc.); remotelycontrol the vehicles 210 (e.g., by transmitting the control instructionsto the vehicle 210 and/or vehicle entity); and/or perform any othersuitable set of operations. The deployment system 220 is preferablyremote from the vehicle 210 (e.g., be a remote processing systemcommunicably connected by a communications system, such as a cellularcommunications system), but can additionally or alternatively beon-board the vehicle 210 and/or otherwise be physically associated withthe vehicle 210 (e.g., where the deployment system 220 includes a remoteprocessing system, a vehicle communications system, and a vehicleprocessing system, etc.). The deployment system 220 is preferablycontrolled by the vehicle entity, but can be otherwise controlled.However, the deployment system 220 can be configured in any suitablemanner.

The event system 230 of the vehicle operation system can function todetect events based on external signals and/or other suitable signals.In particular, the event system 230 can monitor a plurality ofgeographic regions for external signals indicative of occurrence of oneor more events. Each geographic region can include (e.g., encompassfully or partially, etc.) one or more geographic locations (e.g., baseunit of physical location measurement; location referenced by geographiccoordinates; etc.). The event system 230 can be remote from the vehicle210 (e.g., be included in a remote processing system), remote from thedeployment system 220, integrated into the vehicle 210 (e.g., usingdistributed computing) and/or deployment system 220, and/or otherwise bearranged (e.g., physically or logically integrated, etc.).

Geographic regions can additionally or alternatively include geo cells,which can act as a cell in a grid in any form. Geo cells can have anysuitable geometry (e.g., squares, rectangles, spheres, circles,hexagons, triangles, etc.). In a variation, geo cells can be arranged ina hierarchical structure (e.g., be a hierarchical geospatial indexingsystem such as a geohash, etc.), but can additionally or alternativelybe arranged in any suitable structure.

Geo cells are preferably a geocoding system which encodes a geographiclocation into a string of letters and digits (e.g., a code; a shortstring of letters and digits with a number of characters below athreshold; etc.), but can additionally or alternatively encodegeographic locations into any suitable data structures. In an example, ageo cell is a hierarchical spatial data structure which subdivides spaceinto buckets of grid shape (e.g., a square).

In a variation, geo cells can enable arbitrary precision and thepossibility of gradually removing characters from the end of the code toreduce its size, and gradually lose precision. As a consequence of thegradual precision degradation, proximal geographic locations can oftenpresent similar prefixes, but proximal geographic locations can besimilar or dissimilar with respect to any suitable aspects of geo cells.For example, the longer a shared prefix is, the closer the two placesare.

In a variation, geo cells can be used as a unique identifier and torepresent point data (e.g., in databases). In an example, a geo cell isused to refer to a string encoding of an area or point on the Earth. Thearea or point on the Earth may be represented (among other possiblecoordinate systems) as a latitude/longitude or Easting/Northing—thechoice of which is dependent on the coordinate system chosen torepresent an area or point on the Earth. Geo cell can refer to anencoding of this area or point, where the geo cell can be a binarystring comprised of 0s and 1s corresponding to the area or point, or astring comprised of 0s, 1s, and a ternary character (e.g., X)—which isused to refer to a “do not care” character (0 or 1). A geo cell canadditionally or alternatively be represented as a string encoding of thearea or point. For example, one possible encoding is base-32, whereevery 5 binary characters are encoded as an ASCII character.

In examples, depending on latitude, the size of an area defined at aspecified geo cell precision can vary. In a specific example, as shownin Table 1, the areas defined at various geo cell precisions areapproximately:

TABLE 1 Example Areas at Various Geo Cell Precisions geo cellLength/Precision width × height 1 5,009.4 km × 4,992.6 km 2 1,252.3 km ×624.1 km   3 156.5 km × 156 km   4 39.1 km × 19.5 km 5 4.9 km × 4.9 km 6 1.2 km × 609.4 m 7 152.9 m × 152.4 m 8 38.2 m × 19 m   9 4.8 m × 4.8 m10  1.2 m × 59.5 cm 11 14.9 cm × 14.9 cm 12 3.7 cm × 1.9 cm

Additionally or alternatively, geo cell geometries can include hexagonaltiling, triangular tiling, and/or any other suitable geometric shapetiling. For example, the H3 geospatial indexing system can be amulti-precision hexagonal tiling of a sphere (e.g., the Earth) indexedwith hierarchical linear indexes.

In another variation, geo cells can be a hierarchical decomposition of asphere (e.g., the Earth) into representations of regions or points basedon a Hilbert curve (e.g., the S2 hierarchy or other hierarchies).Regions/points of the sphere can be projected into a cube and each faceof the cube includes a quad-tree where the sphere point is projectedinto. After that, transformations can be applied and the spacediscretized. The geo cells are then enumerated on a Hilbert Curve (e.g.,a space-filling curve that converts multiple dimensions into onedimension and preserves the locality). However, geo cells can be basedon any suitable application of Hilbert Curves and/or other suitablecurves.

In variations including geo cells of hierarchical structure, any signal(e.g., external signal), event, entity, vehicle, and/or other suitablecomponent and/or data associated with a geo cell of a specifiedprecision can by default be associated with any less precise geo cellsthat contain the geo cell. For example, if a signal is associated with ageo cell of precision 9, the signal is by default also associated withcorresponding geo cells of precisions 1, 2, 3, 4, 5, 6, 7, and 8 due tothe hierarchical nature of geo cells. Similar mechanisms can beanalogously applicable to other tiling and geo cell arrangements. Forexample, S2 has a cell level hierarchy ranging from level zero(85,011,012 km²) to level 30 (between 0.48 cm² to 0.96 cm²).

The external signals are preferably received from external sources, butcan alternatively be generated by the event system 230 and/or otherwisedetermined. The external signals are preferably received in real- ornear-real time (e.g., as the signals are being generated by or at thesignal source), but can alternatively be received asynchronously fromsignal generation. External signals can include any one or more of:transient or enduring posts authored by users and/or other entities onsocial networking systems (e.g., images, text posts, videos, livestreams, etc.; such as from Facebook™, Twitter™, Snapchat™, and/or othersocial networking systems); weather reports and/or other weather data(e.g., received from metrology systems, secondary systems such as newsoutlets, etc.); emergency response information (e.g., from rapidemergency response systems, 911 call data, dispatch systems, etc.);traffic data (e.g., from traffic cameras or other traffic sensors orsystems); flight tracking data (e.g., FAA data, air operatorcommunication information, etc.); sensor streams from en-route vehicles210 (e.g., forwarded from the deployment system 220, received from thevehicle itself, etc.), which can optionally be using the system 200and/or method 100; other sensor data (e.g., optical sensor data; camerafeeds from public cameras such as CCTV cameras; third party sensor data;listening device feeds; IoT device data; smart city sensor data;satellite data; air quality sensor data; environmental sensor data;etc.) communications from other vehicles (e.g., communications betweenvehicles, such as geographically proximal vehicles, etc.); public radiocommunications (e.g., among first responders and/or dispatchers, betweenair traffic controllers and pilots); other market data (e.g., forcommodities markets, financial markets, etc.); scheduled eventinformation (e.g., from databases of conferences, concerts, sportsgames, or other planned events); remote imaging (e.g., satellites, droneimagery); calendared information; and/or any other suitable signals.Traffic data can include any one or more of: ground traffic data; airtraffic data; accident data (e.g., frequency, rate, type, etc.),crowd-sourced traffic data (e.g., crowd-sourced road information, etc.),traffic level; traffic laws such as no-fly zones; traffic lights; typeof vehicular paths associated with geographic regions; and/or othersuitable traffic data. External signal content can include any one ormore of: images, video, audio, text, files, links, touch-relatedcontent, virtual reality, augmented reality, and/or any other suitabletypes of content.

The signals are preferably received and processed in near-real time, butcan alternatively be batch-processed or otherwise processed. The eventsystem 230 can extract one or more signal parameters (e.g., features;generation timestamp; geolocations; content parameters, such as text,sentiment, objects, object motion vectors; etc.) from the signal.

Each signal is preferably associated with a generation timestamp and ageographic region, and can additionally or alternatively be associatedwith other information (e.g., metadata). The generation timestamp ispreferably indicative of the time the signal was generated (e.g.,authored, created, recorded, etc.), but can additionally oralternatively reference the time that the event, described by thesignal, occurred, or reference any other suitable time. The generationtimestamp can be determined for and/or associated with the signal by thesystem generating the signal, by the event system 230 (e.g., based onthe time the event system 230 receives the signal; based on othertimestamped signals determined by the event system 230 to be related tothe signal; etc.), and/or otherwise determined. The geographic regionassociated with the signal is preferably indicative of where the signalwas generated (e.g., authored, recorded, created, etc.), but canadditionally or alternatively be indicative of where the described eventoccurred. The geographic region associated with the signal is preferablydetermined by the system generating the signal (e.g., the physicallocation of the signal-generating system when the signal was generated),but can additionally or alternatively be generated by the event system230 (e.g., be extracted from the content of the signal, such as anaddress referenced in an emergency response call or the geolocation ofan image included in a social networking post, etc.) and/or otherwisedetermined. When the signal is associated with a geographic regionlarger than a single geographic location, the signal is preferablyassigned to each geographic location within the geographic region. Thesignal can additionally or alternatively be assigned to geographiclocations outside of the geographic region (e.g., based on the contentof the signal). For example, an image of smoke in the distance can beassociated with a geographic location a predetermined distance away fromthe tagged image geolocation (e.g., where the distance can be determinedbased on the type of camera, the zoom degree, the proportion of thesmoke to the remainder of the image, and/or the estimated actual heightof the smoke, as determined from other signals monitoring the same smokesource). However, the signals can be associated with any other suitableset of information, determined in any other suitable manner.

The event system 230 preferably analyzes the geographic regions forevents using the systems and/or methods (e.g., using types of externalsignals) disclosed in U.S. application Ser. Nos. 14/643,958 and15/250,735, which are each incorporated in their entireties by thisreference, but can additionally or alternatively analyze the geographicregions for events in any other suitable manner. The event system 230can monitor: all geographic locations (e.g., every square inch of theworld); a predetermined set of geographic regions (e.g., vehiclelocations, predefined set of locations, etc.), a dynamically determinedset of geographic regions (e.g., determined during vehicular operationalong a vehicle route, etc.), and/or monitor any other suitable set ofgeographic regions or locations for events. In one variation, the eventsystem 230 detects an event in the geographic region when a signalparameter (e.g., frequency of signals, frequency of a content typeextracted from the signal, etc.) satisfies a predetermined pattern(e.g., global or local signal parameter pattern indicative of eventoccurrence). For example, an event can be detected when the signalparameter value increases beyond a reference value (e.g., historicparameter value for the geographic region and recurrent timeframe;threshold value; etc.). In a second variation, the event system 230detects an event in the geographic region when the signal contentsubstantially matches a predetermined value (e.g., a predeterminedkeyword appears in the text, a predetermined object appears in theimage, etc.). Additionally or alternatively, the event can be otherwisedetected from the external signals. However, the event system 230 can beconfigured in any suitable manner.

In a variation, on an ongoing basis, concurrently with signal ingestion(e.g., and also essentially in real-time), event system 230 and/ormonitoring system 240 detect events based on signals. Events can beassociated with a time and location based on the time and location ofsignals used to detect the events. A location can be anywhere across ageographic area, such as, the United States, a State, a defined area, animpacted area, an area defined by a geo cell, an address, and/or anyother suitable representation of a location. Events can be detected froma single signal or from a plurality of signals. In an example, an eventis detected based on the content of one or more signals. In anotherexample, a potential event is detected based on the content of one ormore signals and then validated as an event based on the content of oneor more other signals.

The monitoring system 240 can function to monitor geographic regions forevents of interest to the vehicle 210 and/or vehicle entity (e.g., forthe specific vehicle 210, the vehicle fleet, the vehicle entityassociated with the vehicles 210, etc.). For example, the monitoringsystem 240 can monitor the geographic regions (e.g., for evaluation inrelation to one or more vehicles 210) for a given timeframe (e.g., thevehicle operation period), filter the events detected within thegeographic regions for events of interest to the vehicle class (vehicleclass events) and/or filter the vehicle class events for events ofinterest to the vehicle entity, and facilitate modification of vehicularoperation (e.g., send a notification to the vehicle entity, etc.) whenan event of interest to the vehicle entity (e.g., based on vehicleentity parameters, such as preferences, type, role, responsibilities,demographics, etc.) is detected.

The geographic regions can include the geographic regions along avehicle route (e.g., within a predetermined distance of the vehicleroute); the geographic regions for a plurality of vehicle routes; thegeographic regions specified by a user; the geographic regionsanticipated to be encountered by an en-route vehicle 210 within apredetermined period of time in the future; and/or any other suitableset of geographic regions associated with vehicle operation.

The monitoring system 240 preferably receives event information (e.g.,event parameters, etc.) from the event system 230, but can additionallyor alternatively receive deployment information from the deploymentsystem 220 (e.g., route information), and/or receive any other suitableinformation from any other suitable source. The monitoring system 240preferably provides information to the deployment system (e.g., controlinstructions, notifications, etc.), but can additionally oralternatively provide information to any other suitable source. Themonitoring system 240 can be part of event system 230, complement theevent system 230 (e.g., at a remote processing system), or be separatefrom the event system 230.

The monitoring system 240 preferably includes a set of modules, whichcan function to tailor event of interest determination (and/orsubsequent processes performed based on the event of interest) fordifferent vehicle parameters (e.g., for different vehicles 210, vehicleentities, vehicle classes, etc.) and/or for any other suitablecomponents. The modules can be automatically determined, predetermined,manually received, learned from historic modules of the same or similartype (e.g., include common feature values that all vehicle entitiesrequest), and/or otherwise determined. Each module (e.g., of themonitoring system 240; of any suitable component of the system 200;purposed for performing any suitable portion of the method 100; etc.)can be validated, verified, reinforced, calibrated, and/or otherwiseupdated based on newly received, up-to-date data; historical data;and/or be updated based on any other suitable data.

The modules can optionally include thresholds and/or other conditions(e.g., signal parameter value threshold conditions; notificationthresholds for determining whether to generate and/or providenotifications; etc.), instructions for facilitating vehicular operation(e.g., notification generation instructions, such as to use a first setof notification parameters for an event having a first set of parametervalues; use a second set of notification parameters for an event havinga second set of parameter values; etc.), and/or include any othersuitable aspects for facilitating event of interest determination and/orsubsequent processes performed. In a first variation, each module in theset is associated with a different vehicle 210 or vehicle entity, whereeach module monitors the respective geographic regions, independent ofthe regions monitored by other modules. In a second variation, themonitoring system 240 includes a set of chained modules, where eachmodule serially filters the number of events to be considered. In thisvariation, the geographic regions across all vehicles 210 can beaggregated and monitored together, where the events detected in theaggregate region can be serially filtered to identify events of interestfor a given vehicle 210. However, the monitoring system 240 can beotherwise structured.

In one variation, the monitoring system 240 can include a vehicle classmodule, a vehicle entity module, a vehicle module, and/or any othersuitable set of modules. The vehicle class module can function toidentify events, within the geographic regions, that are of interest toa vehicle class (e.g., affecting movement of the vehicle class). Forexample, the vehicle class module for aerial systems can detect eventsassociated with smoke and news helicopters, but not identify roadconstruction, while a vehicle class module for terrestrial systems candetect events associated with road construction. However, vehicle classmodules can be configured in any suitable manner.

The vehicle entity module can function to identify events that thevehicle entity is interested in (e.g., where event class selections arereceived from the vehicle entity), where the module rules can be learned(e.g., from historic vehicle entity settings, from similar vehicleentities, etc.), received from the user, and/or otherwise determined.The vehicle entity module can be applied to a set of vehicles 210associated with the vehicle entity. However, vehicle entity modules canbe configured in any suitable manner.

The vehicle module can function to identify events specific to thevehicle (e.g., to the vehicle's route), but can be configured in anysuitable manner. Additionally or alternatively, the monitoring system240 can include any suitable set of modules configured in any suitablemanner (e.g., modules tailored to any suitable vehicle parameter).However, the monitoring system 240 can be configured in any suitablemanner.

4. Method.

As shown in FIGS. 1A-1B, embodiments of a method 100 for facilitatingevent-based vehicle operation can include: determining a vehicle routeS100; determining geographic regions for the vehicle route S200;monitoring the determined geographic regions for events S300;determining an event of interest from the detected events S400; and/ordynamically facilitating modification of vehicular operation of thevehicle based on the event of interest S500.

One or more instances and/or portions of the method 100 and/or processesdescribed herein can be performed asynchronously (e.g., sequentially),concurrently (e.g., in parallel; concurrently on different threads forparallel computing to facilitating improved event-based vehicleoperation for a plurality of vehicles, etc.), in temporal relation to atrigger event (e.g., performance of a portion of the method 100), and/orin any other suitable order at any suitable time and frequency by and/orusing one or more instances of the system 200, components, and/orentities described herein. For example, any suitable portions of themethod 100 can be performed (e.g., once, repeatedly, iteratively, etc.)during a time period of vehicular operation of one or more vehicles.Additionally or alternatively, the method 100 can be performed in anysuitable manner analogous to that described in U.S. application Ser.Nos. 14/643,958 and 15/250,735, which are each incorporated in theirentireties by this reference. However, the method 100 can be performedin any suitable manner.

Determining the vehicle route S100 can function to determine informationindicative of where and when a vehicle will be travelling, and/orindicative of any other suitable route parameters and/or associatedvehicle parameters (e.g., vehicle interaction regions in relation to thevehicle route, etc.). The vehicle route can additionally oralternatively determine the minimum set of geographic locations to bemonitored for events (e.g., for reducing computation processingrequirements, etc.). The vehicle route can be 2D (e.g., projected onto amap), 3D (e.g. include a vertical component; account for terrain; etc.),or have any suitable set of dimensions (e.g., include dimensions relatedto route parameters and/or vehicle parameters).

The vehicle route is preferably determined based on route parameters.The route parameters are preferably determined for a single vehicle, butcan additionally or alternatively be determined for a plurality ofvehicles (e.g., vehicle fleet, multiple vehicle fleets, etc.). Forexample determining vehicle routes can include determining waypoints fora plurality of vehicles, generating an accounting for potentialvehicular travel overlap based on the waypoints (e.g., arrival ofvehicles at proximal waypoints and overlapping time periods, such aswhere the vehicle interaction regions at the proximal waypoints overlapwith each other, etc.); and adjusting vehicle routes based on thepotential vehicular travel overlap. The route parameters can includethose discussed above, or include any other suitable set of parameters.The route parameters can be received from the deployment system (e.g.,imported, synchronized with the deployment system, received through acommunication from the deployment system, received from a third partydeployment system, etc.), received from a user, selected based on a setof vehicle parameters, received from the vehicle (e.g., calculated basedoff of sensor data sampled at the vehicle, etc.), automaticallydetermined (e.g., from a set of predetermined waypoints, example shownin FIG. 3, the respective waypoint arrival times, and the on-going oranticipated events proximal the route), and/or otherwise determined. Inexamples, determining a vehicle route can be based on route parametersincluding one or more route optimization parameters, which can functionto inform vehicle route determination to optimize for one or more of:travel time, battery life, fuel, durability, event of interest avoidanceor attendance, and/or any other suitable parameters (e.g., suitablevehicle parameters and/or route parameters, etc.).

Additionally or alternatively, determining vehicle routes S100 can bebased on any suitable data described herein. For example, determining avehicle route can include determining adjustments to a vehicle routebased on event parameters generated for a detected event of interest(e.g., adjusting a route that vehicle is currently traveling along toavoid an event of interest by a distance greater than a distancethreshold derived from the vehicle interaction region, etc.).

Determining vehicle routes S100 can be performed once, repeatedly,and/or with any suitable frequency for one or more periods of vehicularoperation. For example, the method 100 can include determining aninitial vehicle route (e.g., before vehicle deployment) for the vehicle(e.g., based on route parameters collected by a vehicle entity; based onan initial event classified as an initial event of interest for thevehicle; etc.), and during the time period of the vehicular operation ofthe vehicle along the initial vehicle route, determining an updatedvehicle route based on a current event of interest, the declassificationof the initial event as an event of interest (e.g., based on externalsignals collected during vehicular operation, etc.) and/or othersuitable data. In examples, dynamically facilitating modification of thevehicular operation (e.g., providing control instructions, determiningnotifications, providing adjusted vehicle routes, etc.) can be based onevents of interest, declassifications of events of interest (e.g.,events of interest that were previously used in determining a vehicleroute, etc.), and/or other suitable data. However, determining vehicleroutes can be performed at any suitable time and frequency.

In a first variation, the vehicle route is received from the deploymentsystem (e.g., as shown in examples in FIGS. 16, 17, and 19). In a secondvariation, the vehicle route is determined by connecting waypoints withstraight lines (e.g., as shown in an example in FIG. 18). In a thirdvariation, determining the vehicle route includes computing an optimalpath between a set of waypoints (e.g., optimizing for travel time,travel cost, etc.) based on optimization parameters, while satisfyingtemporal conditions (e.g., waypoint arrival times) and/or obstacleconditions (e.g., on-going and/or anticipated events to avoid, obstaclesto avoid, etc.) and/or other suitable conditions. However, the vehicleroute can be otherwise determined in any suitable manner.

Determining the geographic regions for the vehicle route S200 canfunction to identify the geographic regions (e.g., limit the number ofgeographic regions, for improving computational efficiency, etc.) thatare monitored for events. The geographic regions (e.g., geographicregions) are preferably identified based on the route parameters, butcan be otherwise determined (e.g., based on vehicle parameters such asvehicle sensor data indicating vehicle location and/or movement, etc.).The geographic regions can be static (e.g., predetermined, locked to theroute), dynamic (e.g., move with the vehicle), and/or otherwisedetermined. The geographic regions are preferably automaticallyidentified, but can additionally or alternatively be manually identifiedand/or otherwise determined. The geographic regions are preferablydetermined by the monitoring system, but can alternatively be determinedby the vehicle entity, event system, deployment system, vehicle, user,and/or by any other suitable system. The geographic regions can beidentified: in response to vehicle route determination; in response toroute parameter determination; periodically (e.g., at a predeterminedfrequency, while the vehicle is en-route, during other periods ofvehicular operation etc.); dynamically (e.g., in response to occurrenceof a geographic region identification event, continuously as a vehicletravels along a vehicle route, etc.); and/or at any other suitable timeand frequency. In a first example, geographic regions can be determinedevery minute for the anticipated travel path to be covered in the next5-10 minutes (e.g., based on the current vehicle location and motionparameters, such as acceleration and velocity), example shown in FIGS. 7and 8. In a second example, geographic regions can be determined when anobstacle is detected in the vehicle path (e.g., from signals sampled byon-board sensors), when an event is detected proximal the vehicle path,or in response to occurrence of any other suitable geographic regionidentification event. In a third example, determining geographic regionscan be performed at multiple times during a period of vehicularoperation, such as where the method 100 can include dynamicallydetermining a set of geographic regions based on a vehicle route duringthe time period of the vehicular operation, and in response todynamically facilitating modification of the vehicular operation (e.g.,controlling a vehicle to travel along an adjusted vehicle route,determining an adjusted vehicle route, etc.), determining an updated setof geographic regions for event monitoring based on the adjusted vehicleroute.

Geographic regions can be determined for: the entire vehicle route, fora vehicle route segment (e.g., time-limited, distance-limited,waypoint-limited), and/or for any other suitable portion of the vehicleroute. Geographic regions can be determined for: a single trip for asingle vehicle, multiple trips for a single vehicle, a single route(e.g., that multiple vehicles traverse), multiple trips for multiplevehicles, or for any suitable time period, route, and/or set ofvehicles.

Determining the geographic regions S200 can include: selecting,calculating, estimating, or otherwise determining the geographicregions. Determining the geographic regions can be based on vehicleinteraction regions (e.g., determining initial vehicle routes, adjustedvehicle routes, and/or other suitable vehicle routes based on vehicleinteraction regions, etc.). For example, determining the geographicregion can include: using the vehicle interaction region (VIR) as thegeographic region; sweeping a predetermined region or volume (e.g., theVIR) along the route; determining an interaction region for each of aset of points along the route (e.g., every geographic location, everypredetermined route distance, etc.) based on the point location, vehiclevelocity, arrival time, proximal events, or other variables; selecting aset of pre-defined geofences that cooperatively encompass the route(e.g., geofences already monitored by the monitoring system, geofencesalready monitored by the event system, etc.); or otherwise determiningthe geographic region. Multiple determined regions can have the same ordiffering shape, size, or other parameter.

Determining the geographic region S200 can optionally include:aggregating geographic regions (e.g., merging multiple determinedregions, etc.); segmenting the resultant region (e.g., merged,originally determined, example shown in FIG. 4) into sub-regions (e.g.,defining contiguous and/or overlapping geographic regions cooperativelyencompassing the vehicle route, as shown in an example in FIG. 6);identifying the geographic locations encompassed by the determinedregion (e.g., monitoring the individual geographic locations, as shownin an example in FIG. 5); or otherwise processing the determinedregions. At any given time, the system 200 can monitor, for a givenvehicle: one determined region (e.g., merged region, sub-region, etc.),multiple adjacent regions, multiple separate regions, or any suitableset of regions. The geographic region can be the region encompassing thevehicle, adjacent the vehicle (e.g., to be travelled within a thresholdtime period, within the same leg of the trip, etc.), distal the vehicle,or be any other suitable region. Geographic regions in the set can beconstantly monitored or selectively monitored (e.g., based on thevehicle location and/or motion parameters; move with the vehicle,example shown in FIG. 8; etc.). However, the geographic regions can beotherwise determined.

The method 100 can additionally or alternatively include aggregatinggeographic regions (e.g., geographic regions determined across multiplevehicle routes, such as for a plurality of vehicles; geographic regionsdetermined for a single vehicle route; etc.) which can function tominimize and/or eliminate redundancies in monitored geolocations acrossthe set of monitored routes (e.g., for improving the processing systemitself; etc.). For example, the method 100 can include aggregating a setof geographic regions into an aggregate region (e.g., based onoverlapping geographic regions and/or other suitable parameters, etc.)associated with different vehicle routes; collecting external signalsfor the aggregate region; and detecting a set of events associated withthe aggregate region (e.g., where the event locations are within theaggregate region, etc.) based on the external signals. The multipleroutes associated with geographic region aggregation can be for the samevehicle, multiple vehicles (e.g., with similar waypoint arrival times;associated with routes within a threshold time period of each other;etc.), and/or for any suitable set of vehicles and/or time period. Inone variation, the geographic regions are aggregated by aggregating thegeographic locations for the multiple routes, then determining one ormore geographic super-regions collectively encompassing all or amajority of the locations. In a second variation, the geographic regionsare aggregated by adding the geographic regions for the multiple routesto a common list or map, or merging contiguous geographic regionstogether. However, the geographic regions can be aggregated in any othersuitable manner.

Aggregating geographic regions can optionally include selecting trips(or portions thereof) for aggregation (as shown in an example in FIG.9). In one variation, the trips selected for aggregation includesegments of different vehicle routes sharing a common time period (e.g.,all selected vehicle route segments will be travelled within the next 20minutes), where the time period and/or time duration can be determinedmanually, automatically, and/or otherwise determined. For example,aggregating geographic regions can include identifying overlappinggeographic regions (e.g., with any suitable amount of geographicoverlap, etc.) including a first geographic region and a secondgeographic region associated with a first vehicle route and a secondvehicle route, respectively; estimating a first time period for arrivalof a first vehicle of a set of vehicles at the first geographic region;estimating a second time period for arrival of a second vehicle of theset of vehicles at the second geographic region; and in response to thesecond time period being within a threshold time duration of the firsttime period (e.g., where vehicle interactions regions for the first andsecond vehicles would overlap, etc.), aggregating the first and secondoverlapping geographic regions into the aggregate region.

In a second variation, the trips selected for aggregation includesegments of different vehicle routes within a predetermined geofence ordistance of each other (e.g., all selected vehicle route segments arewithin 10 mi of each other), where the geofence or distance can bemanually determined, automatically determined, and/or otherwisedetermined. In a third variation, the trips selected for aggregationinclude trips from a common vehicle class (e.g., aggregating geographicregions from all or a portion of aerial vehicles, drones, helicopters,vehicles with similar vehicle interaction regions, etc.) and/or othersuitable vehicle parameter (e.g., sharing a module type used for eventof interest identification, etc.). In a fourth variation, the tripsselected for aggregation include trips from a common vehicle entity. Ina fifth variation, aggregating geographic regions can include updatingaggregate regions (e.g., as vehicles travel along vehicle routes, asvehicle routes are adjusted, etc.). In an example (e.g., as shown inFIG. 10B), the method 100 can include: determining an adjusted vehicleroute (e.g., as part of dynamically facilitating vehicular operationmodification, etc.) for a vehicle based on an event of interest for thevehicle, determining an updated set of geographic regions for thevehicle based on the adjusted vehicle route; and determining an updatedaggregate region based on overlap between the updated set of geographicregions and the aggregate region (e.g., an aggregate region initiallydetermined and used in association with determining the event ofinterest, etc.).

Additionally or alternatively, the trips can be otherwise selected foraggregation, and/or aggregating geographic regions can be based on anysuitable parameters. However, aggregating geographic regions can beperformed in any suitable manner, and determining geographic regions canbe performed in any suitable manner.

Monitoring the determined geographic regions for events S300 canfunction to identify the occurrence of events of interest associatedwith the vehicle route (e.g., proximal the vehicle route, etc.), suchthat the vehicle can be routed for avoiding or traveling to the eventlocation (and/or associated geographic regions), such as while the eventis on-going and/or has the ability to affect (e.g., interact) withvehicles. The geographic regions are preferably monitored by themonitoring system, but can alternatively be monitored by the eventsystem and/or by any other suitable system. The geographic regions arepreferably monitored while the vehicle is deployed (e.g., en-route),where the geographic regions can be monitored at a predeterminedfrequency, when new signals are received, or at any other suitable time,but can additionally or alternatively be monitored for a predeterminedperiod before and/or after the vehicle is deployed, monitored for apredetermined period before and/or after the vehicle reaches thegeographic region, or monitored at or for any other suitable time and/ortime period.

Monitoring the determined geographic regions for events S300 caninclude: receiving signals from external sources; determining thegeographic region for each signal; extracting event features from theset of signals mapped to each geographic region; and detecting an eventbased on the event features for the geographic region. However, thegeographic regions can be otherwise monitored.

Receiving signals from external sources can function to obtain rawand/or processed signals potentially indicative of one or more eventparameters, for determining events of interest, for facilitatingvehicular operation modification, and/or for performing other portionsof the method 100. The signals (e.g., external signals, etc.) preferablyinclude those described herein, but can include other signals. Thesignals are preferably received by the event system but can be receivedby any other suitable system. The signals are preferably received inreal- or near-real time, but can alternatively be received at any othersuitable time and frequency. Signal collection for one or moregeographic regions of a set of geographic regions can be based on (e.g.,conditioned based on), temporal indicators (e.g., collecting externalsignals for a predetermined period of time), vehicle parameters (e.g.,collecting external signals for a geographic region while a vehiclelocation is within a threshold distance of the geographic region, etc.),route parameters, and/or any other suitable data. For example,collecting external signals for a set of geographic regions can include:during a first time period within a time period of the vehicularoperation, collecting first signals for a first subset of geographicregions of the set of geographic regions based on first vehicle sensordata (e.g., indicating geographic proximity of the vehicle from thefirst subset of geographic regions, etc.) sampled by the first vehicleduring the first time period; during a second time period within thetime period of the vehicular operation: ceasing signal collection (e.g.,to reduce computational processing requirements, etc.) for the firstsubset of geographic regions based on second vehicle sensor data sampledby the first vehicle during the second time period (e.g., indicatingthat the vehicle is at a geographic distance exceeding a thresholddistance from the first subset of geographic regions; etc.); andcollecting second external signals for a second subset of geographicregions of the set of geographic regions (e.g., to detect and/orcharacterize additional events for the second subset of geographicregions, etc.). However, collecting external signals can be performed inany suitable manner.

Determining the geographic region for each signal can function to mapthe raw signal to a geographic region (e.g., for identifying thegeographic region at which a signal was collected, which can be used tofacilitate determination of a geographic region for which to map one ormore events; etc.). In one variation, the signal is mapped to a singlegeographic location (e.g., GPS coordinate, geographic base unit, etc.),such as where an event detected based off of at least the signal can bemapped to the same geographic location and/or geographic regionassociated with the geographic location (e.g., encompassing thegeographic location; etc.). In this variation, events can be detectedfor a monitored geographic region by identifying events associated withgeographic locations encompassed by the monitored geographic region(e.g., where the signals, used in detecting the event, correspond to thegeographic locations, etc.). In a second variation, the signal is mappedto a geographic region encompassing a set of geographic locations. Inthis variation, the signal (and/or extracted feature values) can bemapped to the geographic region as a whole, mapped to each geographiclocation within the geographic region, or be mapped to any othersuitable geographic representation. In one embodiment, the geographicregion is a monitored geographic region. In a second embodiment, thegeographic region is the signal's geographic region (e.g., geographicregion that the signal is tagged with). For example, the geographicregion can be defined by the radius of uncertainty centered on ageographic location. In a second example, the geographic region can bedefined by the geopolitical and/or physical boundaries associated withthe geographic identifier for the signal (e.g., the building associatedwith the address mentioned in the emergency response call; the cityassociated with the social networking system post; etc.). Additionallyor alternatively, the signals, events, associated data (e.g., geographicregion tags for signals, etc.) and/or other suitable data and/orcomponents can be mapped to any suitable geographic region in anysuitable manner. However, monitoring geographic regions S300 can beperformed in any suitable manner.

Extracting event features from the set of signals can function todetermine values that can be used to compute whether an event isoccurring in the geographic region. The event features are preferablydetermined for each geographic region, from the signals mapped to therespective geographic region (e.g., indicating that the eventcorresponding to the signals is occurring at or proximal the geographicregion, etc.), but can alternatively be determined from any othersuitable set of signals. The event features are preferably extractedfrom signals associated with the same time period (e.g., generated orreferencing a time within is of each other), but can alternatively beextracted from any other suitable set of signals. Event features can beextracted from the content of the signals, the characteristics of thesignal set (e.g., frequency, temporal or spatial distribution), or fromany other suitable aspect of the signal set. The event features can beextracted using regression, classification, neural networks (e.g.,convolutional neural networks), heuristics, equations (e.g., weightedequations, etc.), selection (e.g., from a library), instance-basedmethods (e.g., nearest neighbor), regularization methods (e.g., ridgeregression), decision trees, Bayesian methods, kernel methods,probability, deterministics, or any other suitable method.

Detecting the event based on the event features for the geographicregion can function to determine that an event has occurred within thegeographic region. The event can be an on-going event (e.g., currentevent), past event, or future event (e.g., anticipated event). The eventcan be detected for a geographic region (e.g., where the event locationwithin the geographic region can be subsequently determined), ageographic location (e.g., where the events for all geographic locationscan be aggregated when monitoring a geographic region), or for any othersuitable set of geographic locations.

In a first variation, an event is detected based on the signal strength(e.g., post frequency, feature value, event probability, anomalyprobability) for a geographic region, where an event is detected whenthe signal strength exceeds a threshold signal strength. The thresholdsignal strength can be the historic signal strength for the respectivegeographic region and timeframe, be a manually-determined threshold forthe respective geographic region and/or timeframe, be the adjacentregion's signal strength, or be any other suitable threshold. In aspecific example, monitoring the geographic regions for events caninclude collecting a set of posts from a set of social networkingsystems (and/or collecting other suitable external signals) assigning asubset of posts from the set of posts to one or more geographic regionsof the set of geographic regions; determining a keyword frequency(and/or other suitable post-related feature) for the subset of posts;and detecting an event for one or more geographic regions in response tothe keyword frequency exceeding a historic keyword frequency (and/orother suitable post-related feature value exceeding a historicalpost-related feature value) for the one or more geographic regions.

Additionally or alternatively, an event can be detected when the signalstrength exceeds the threshold signal strength for a threshold period oftime (e.g., a manually period of time, a learned period of time, etc.).

In a second variation, an event is detected when the signal patterns(e.g., temporal, spatial, etc.) substantially matches a predeterminedpattern associated with an event or event category (e.g., event type).The predetermined pattern can be learned (e.g., based on past identifiedevents for the geographic region and/or other geographic regions),manually determined, and/or otherwise determined.

In a third variation, an event is detected based on the signal content.For example, an event can be detected when the signal includes contentpre-associated with event occurrence (keywords, atypical objects for thelocation).

In a fourth variation, an event is detected when signals from aspecified source are received. For example, an event can be detected fora geographic region whenever an emergency stream mentions and/ororiginates from a location encompassed by the geographic region.

In a fifth variation, an event is detected by applying regression,classification, neural networks (e.g., convolutional neural networks),heuristics, equations (e.g., weighted equations, etc.), selection (e.g.,from a library), instance-based methods (e.g., nearest neighbor),regularization methods (e.g., ridge regression), decision trees,Bayesian methods, kernel methods, probability, deterministics, or anyother suitable method to the extracted event features for the geographicregion.

Determining the event can additionally or alternatively includedetermining event parameters for the detected event, which can functionto determine parameters describing the event. The event parameters canbe determined before, after, or during event detection. Event parameterscan include: whether an event is occurring (e.g., a binaryclassification, a probability, etc.), the event category (e.g., traffic,sports, games, accidents, fire, natural disasters, entertainment,concerts, no-fly zones, wireless signal dead-zones, etc.), eventseverity, event truthfulness, event content (e.g., event title, eventdescription, etc.), event time (e.g., estimated and/or anticipated starttime, end time, duration, etc.), event location (e.g., center; nexus),event extent or volume (e.g., event geofence, geographic region,physical dimensions effected, etc.), event entities (e.g., usersproximal the event or otherwise related to the event), associatedconfidence levels (e.g., for other event parameters, etc.) and/or anyother suitable event parameters. The event parameters can be determinedbased on the extracted features, signal content, or based on any othersuitable information. The event parameters can be determined usingregression, classification, neural networks (e.g., convolutional neuralnetworks), heuristics, equations (e.g., weighted equations, etc.),selection (e.g., from a library), instance-based methods (e.g., nearestneighbor), regularization methods (e.g., ridge regression), decisiontrees, Bayesian methods, kernel methods, probability, deterministics, orany other suitable method. In one example, the event can be classifiedand the event extent determined based on the event class. In a secondexample, the event extent can be determined from the proportion of animage frame occupied by smoke. In a third example, the event extent canbe determined from signals, generated within a predetermined time frameof the event time, that are mapped to geographic regions surrounding theevent location. In a fourth example, determining event parameters for aset of events can include, for each event of the set of events:determining a category probability for an event category based onsignals of the external signals, the signals associated with the event(e.g., signals assigned to geographic regions corresponding to theevent; etc.); in response to the category probability exceeding acategory probability threshold, categorizing the event with the eventcategory, where determining the event of interest from the set of eventscan be based on the event category (and/or the vehicle parameterassociated with the vehicle, and/or other suitable data, etc.). In afifth example, events can be categorized into different categories(e.g., fire, police response, mass shooting, traffic accident, naturaldisaster, storm, active shooter, concerts, protests, etc.) based on thecontext of signals used to detect the events. In a sixth example, anevent truthfulness can be determined for an event (e.g., by an eventsystem 230 and/or monitoring system 240, etc.). An event truthfulnesscan indicate how likely it is that a detected event is actually an event(versus a hoax, fake, misinterpreted, etc.). In specific examples,truthfulness can range from less likely to be true to more likely to betrue; and truthfulness can be represented as a numerical value (e.g., ascore), such as, for example, from 1 (e.g., less truthful) to 10 (e.g.,more truthful) and/or as percentage value in a percentage range, suchas, for example, from 0% (e.g., less truthful) to 100% (e.g., moretruthful). However, event truthfulness can be represented in anysuitable manner. In a seventh example, an event severity can bedetermined for an event. Event severity can indicate how severe an eventis (e.g., what degree of badness, what degree of damage, etc. isassociated with the event), was, and/or will be. In a specific example,severity can range from less severe (e.g., a single vehicle accidentwithout injuries) to more severe (e.g., multi vehicle accident withmultiple injuries and a possible fatality). In another specific example,a shooting event can also range from less severe (e.g., one victimwithout life threatening injuries) to more severe (e.g., multipleinjuries and multiple fatalities). Severity can be represented as anumerical value, such as, for example, from 1 (e.g., less severe) to 5(e.g., more severe), and/or any other suitable representations.

Event parameters are preferably used in dynamically facilitatingmodification of vehicular operation, but can be used in any suitableportion of the method 100 and/or system 200. For example, a vehicleroute optimizing for safety and/or other parameters can be determined toavoid events with an event severity greater than a threshold value(e.g., a low threshold, such as 10%, etc.), and/or an event truthfulnessgreater than a threshold value (e.g., a low threshold, such as 10%,etc.). In another example, a vehicle route can be determined toinvestigate events with an event truthfulness greater than a thresholdvalue (e.g., greater than a predetermined or dynamically determinedpercentage probability of being true). However, event parameters can beutilized in any suitable manner.

Additionally or alternatively, event parameters can be determined in anysuitable fashion described in and/or analogous to that described in U.S.application Ser. Nos. 14/643,958 and 15/250,735, which are eachincorporated in their entireties by this reference. However, the eventparameters can be otherwise determined.

Determining an event of interest from the detected events S400 canfunction to surface events relevant to a vehicle and/or vehicle entity(e.g., for highlighting particular events out of a set of detectedevents, to better facilitate improved event-based routing, etc.).Determining events of interest S400 is preferably based on vehicleparameters and/or event parameters (e.g., based on analyzing vehicleinteraction regions and/or other parameters describing susceptibility ofvehicles to events, in the context of event parameters informative ofthe event; etc.), but can additionally or alternatively be based on anysuitable data.

Determining events of interest S400 can including determining events ofinterest with one or more modules (e.g., vehicle modules, such as forevent filtering, etc.). For example, determining an event of interestcan include filtering the set of events for the event of interest with avehicle module (e.g., vehicle class module) of the set of vehiclemodules (e.g., the vehicle module corresponding to the vehicle parameterassociated with the vehicle, such as the vehicle class associated withthe vehicle, etc.).

Any number of events of interest can be determined for any number ofvehicles, vehicle entities, and/or other suitable components. Forexample (e.g., as shown in FIG. 10A), determining events of interest caninclude filtering the set of events for a first vehicle event ofinterest based on a first vehicle parameter associated with a firstvehicle; filtering the set of events for a second vehicle event ofinterest based on a second parameter associated with the second vehicle;and where filtering the same set of events for the first and secondvehicles improves computational efficiency of the processing system forfacilitating the improved event-based vehicle routing for the set ofvehicles.

Determining one or more events of interest S400 can be performed inrelation to a trigger event (e.g., in response to detecting an event; inresponse to detecting a threshold number of events and/or type of event;vehicle deployment; vehicle arrival at a particular waypoint; etc.), atpredetermined time intervals (e.g., every 5 minutes; etc.), and/or atany suitable time and frequency.

In a variation, the method 100 can include generating one or modules,such as based on vehicle parameters (e.g., associated with a set ofvehicles, etc.), event parameters (e.g., historical event parameters,etc.), route parameters, and/or any other suitable data. In an example,the method 100 can include for each vehicle class of the differentvehicle classes, determining a vehicle interaction region associatedwith environmental interaction for vehicles within the vehicle class;and for each vehicle class module of the set of vehicle class modules,determining the vehicle class module based on the vehicle interactionregion for the corresponding vehicle class. However, generating,updating, and/or otherwise processing modules can be performed in anysuitable manner, and determining events of interest can performed in anyother suitable manner.

Dynamically facilitating modification of vehicular operation of thevehicle S500 can function to facilitate improvements in vehicle routingand/or other aspects of vehicular operation. Facilitating modificationof the vehicular operation can include any one or more of: controllingone or more vehicles, determining an adjusted vehicle route, determininga notification, providing an event content stream with the notification,and/or any other suitable processes.

Facilitating vehicular operation modification S500 is preferably basedon one or more events of interest (e.g., event parameters describing theevents of interest) and/or vehicle parameters (e.g., current vehiclelocation, vehicle interaction region, etc.), but can be additionally oralternatively be based on any suitable data.

Facilitating modification of vehicular operation S500 can be performedfor a single vehicle (e.g., the vehicle for which geographic regionswere determined and events were detected, etc.), a plurality of vehicles(e.g., vehicles for which the same event of interest applies; vehiclesassociated with each other, such as through shared vehicle parameters;etc.), a single vehicle entity, multiple vehicle entities, and/or anynumber of entities. For example, facilitating modification of vehicularoperation can include during a second vehicle time period associatedwith a second vehicle, dynamically facilitating re-routing of the secondvehicle based on the event parameters, where the event parametersinclude an estimated end time of the event of interest (e.g., the eventof interest identified for a first vehicle during a first vehicle timeperiod, etc.). In a specific example, the method 100 can includeestimating, for the second vehicle, a waypoint arrival time associatedwith a geographic region of the set of geographic regions based on asecond vehicle parameter (e.g., parameters describing position,velocity, and/or acceleration, etc.), the geographic regioncorresponding to the event of interest; and in response to the waypointarrival time being earlier than the estimated end time of the event ofinterest (e.g., where the vehicle interaction region would overlap withan event region of effect, etc.), dynamically facilitating modificationof vehicular operation of the second vehicle (e.g., re-routing of thesecond vehicle, etc.).

Facilitating modification of vehicular operation S500 can additionallyor alternatively include controlling one or more vehicles (e.g., one ormore aspects of vehicular operation, etc.), which can function toimprove vehicular operation (e.g., based on real-time data). Controllingone or more vehicles is preferably performed remotely (e.g., through aremote processing system), such as by generating control instructionsfor the vehicle, and transmitting the control instructions to thevehicle for execution by the vehicle processing system, but controllingvehicles additionally or alternatively can be performed locally (e.g.,with a driver located in the vehicle, etc.), and/or in any suitablemanner. In an example, the method 100 can include determining controlinstructions for the vehicle based on the adjusted vehicle route;facilitating remote control of the vehicle to travel along the adjustedvehicle route based on the control instructions; and monitoring updatedgeographic regions (e.g., an aggregate region updated based on theadjusted vehicle route, etc.) for additional events during the remotecontrol of the vehicle. However, controlling one or more vehicles can beperformed in any suitable manner.

Facilitating modification of vehicular operation S500 can additionallyor alternatively include determining an adjusted vehicle route (e.g., asshown in FIG. 13), which can function to dynamically improve vehiclerouting in relation to an event of interest (e.g., avoiding or attendingan event of interest, etc.) Determining adjusted vehicle routes caninclude: classifying event as adverse, treating adverse events asno-traverse zones (e.g., no-fly, no-drive, no-sail zones), anddetermining new waypoints and/or routes that maintain a thresholddistance between the event zone and the vehicle (e.g., using pathoptimization techniques, path planning, etc.). The threshold distancecan be predetermined, be the interaction region, or be any othersuitable region. Additionally or alternatively, the event can beclassified as favorable and treated as a new waypoint (e.g., where thevehicle is routed to the event nexus or to a threshold distance of theevent nexus). However, the vehicle route can be otherwise dynamicallyadjusted based on the detected events and/or event parameters.

Dynamically facilitating modification of vehicular operation S500 canadditionally or alternatively include determining a notification (e.g.,based on a detected event of interest, etc.), which can function todetermine whether and what notifications about the event and/orassociated aspects should be provided (e.g., to vehicles, vehicleentities, etc.). The notification is preferably generated and/or sentwhen an event of interest is detected within the monitored geographicregions (e.g., when the vehicle interaction region is anticipated toencompass event location while the detected event is on-going), but thenotification can be sent at any other suitable time and frequency.

The notification can include an icon (as shown in examples in FIGS.20-22), a list (e.g., color-coded; ordered by importance or distance; asshown in examples in FIGS. 20, and 22), a text notification, aprogrammatic notification, an actionable notification (e.g., where thenotification presents one or more action options to the user), operationinstructions, or any other suitable components. The notificationparameters (e.g., transmission time, size, color, audio, etc.) can bepredetermined, determined based on the event's actual or anticipatedphysical proximity to the vehicle, the event class, the probability ofthe event affecting the planned route, the type of effect the event willhave on vehicle operation (e.g., force route change, will slightlydelay), the event's temporal relationship to the vehicle (e.g.,probability of terminating by the time the vehicle reaches the eventlocation), and/or otherwise determined. The notification can begenerated by the monitoring system and/or any other system, and sent tothe deployment system, the vehicle, a user device, or any other suitablesystem.

In a first variation, determining the notification includes detectingthe event in a geographic region (e.g., monitored location), identifyingthe vehicles associated with the geographic region (e.g., vehiclescurrently located within the geographic region, vehicles with aninteraction region overlapping the geographic region, vehicles withinteraction regions that will overlap the geographic region within athreshold period of time, etc.), characterizing the event for a vehicleusing modules associated with the identified vehicles, and notifying thevehicle entity (e.g., at a processing system, a user device, anauxiliary vehicle, etc.) when the event satisfies the module conditions(example shown in FIGS. 11 and 12).

In a second variation, determining the notification includes detectingan event in a geographic region; determining whether the event is ofinterest to a vehicle class (e.g., using a vehicle class module); whenthe event is of interest to a vehicle class, determining the eventlocation; identifying the vehicles associated with the event location(e.g., within or will be within a predetermined distance of the eventlocation); optionally determining whether the event is of interest tothe vehicle (e.g., based on a vehicle or vehicle entity module); andnotifying the vehicles and/or vehicle entities of the event (exampleshown in FIG. 13).

In a third variation, determining the notification includes applyingonly event-detection methods associated with the vehicle class, vehicleentity, and/or vehicle to the geographic region, such that only relevantevents are detected.

Any suitable number and/or type of notifications can be determinedand/or provided to any suitable number of entities (e.g., vehicles,vehicle entities, etc.). For example, providing notifications caninclude: in response to detecting a first vehicle event of interest,determining a first route-related notification (e.g., presenting agraphical indicator of the event of interest at a map displaying thevehicle route, etc.) based on the first vehicle event of interest; inresponse to detecting the second vehicle event of interest, determininga second route-related notification based on the second vehicle event ofinterest; and simultaneously presenting the first and secondroute-related notifications to a vehicle entity associated with thefirst and second vehicles (e.g., a vehicle entity controlling, managing,and/or otherwise associated with the vehicles, etc.). In anotherexample, the method 100 can include associating the event of interestwith a second vehicle based on a second vehicle route for the secondvehicle (e.g., identifying that the event is an event of interest to asecond vehicle in addition to being an event of interest to a firstvehicle, etc.), determining a route-related notification based on theevent of interest; and transmitting the route-related notification to afirst vehicle entity and a second vehicle entity associated with thefirst and second vehicles, respectively. However, determining and/orproviding notifications can be performed in any suitable manner.

Facilitating modification vehicular operation S500 can additionally oralternatively include providing an event content stream with thenotification, which can function to present notifications in a manneradapted for improved viewing (e.g., as shown in an example in FIG. 20).In this variation, the method 100 can include: identifying the signalsassociated with the detected event, aggregating the signals into anevent content stream (e.g., in real- or near-real time) and presentingthe event content stream with the notification. The event content streamcan optionally be linked to an event icon on a map, where icon selectioncan open the event content stream. However, providing event contentstreams can be performed in any suitable manner, and dynamicallyfacilitating modification of vehicular operation S500 can be performedin any suitable manner.

Additionally or alternatively, data described herein (e.g., vehicleroutes, geographic regions, vehicle interaction regions, eventparameters, modules, external signals, vehicle data, etc.) can beassociated with any suitable temporal indicators (e.g., seconds,minutes, hours, days, weeks, etc.) including one or more: temporalindicators indicating when the data was collected (e.g., from a vehicleentity, from external sources, etc.), determined, transmitted, received,and/or otherwise processed; temporal indicators providing context tocontent described by the data, (e.g., temporal indicators indicating theduration of an event of interest; temporal indicators indicating overlapin time periods between an event of interest duration and a vehiclearrival at a geographic region proximal the event of interest; etc.);changes in temporal indicators (e.g., data over time; change in data;data patterns; data trends; data extrapolation and/or other prediction;etc.); and/or any other suitable indicators related to time.

Additionally or alternatively, parameters, metrics, inputs, outputs,and/or other suitable data can be associated with value types including:scores (e.g., indicating a relevance level of an event to a vehicleand/or vehicle parameter, such as for identifying events of interest;describing a characteristic of an event, such as a danger score; etc.),binary values (e.g., whether or not an event exists, etc.),classifications (e.g., event categories, vehicle classes, geographicregion types, etc.), confidence levels (e.g., probability values fordetection of an event and/or for event parameter determination, etc.),values along a spectrum, values with any suitable units of measurement(e.g., metric system units, US customary units, etc.), geometricdescriptors (e.g., geographic region radius, aggregate region area,event area, route descriptions, etc.), and/or any other suitable typesof values. Any suitable types of data described herein can be used asinputs (e.g., for different system components described herein, such asmodules; for portions of the method 100; etc.), generated as outputs(e.g., of system components), and/or manipulated in any suitable mannerfor any suitable components associated with the method 100 and/or system200.

Additionally or alternatively, components of the system 200 (e.g.,modules, event system, monitoring system, vehicles, etc.) and/orsuitable portions of the method 100 (e.g., detecting events, determiningevents of interest, dynamically facilitating vehicular operationmodification, etc.) can apply processing techniques including any one ormore of extracting features, performing pattern recognition on data,fusing data from multiple sources, combination of values (e.g.,averaging values, etc.), compression, performing statistical estimationon data (e.g. ordinary least squares regression, non-negative leastsquares regression, principal components analysis, ridge regression,etc.), wave modulation, normalization, updating, ranking, weighting,validating, filtering (e.g., for baseline correction, data cropping,etc.), noise reduction, smoothing, filling (e.g., gap filling),aligning, model fitting, binning, windowing, clipping, transformations,mathematical operations (e.g., derivatives, moving averages, summing,subtracting, multiplying, dividing, etc.), data association,multiplexing, demultiplexing, interpolating, extrapolating, clustering,image processing techniques (e.g., image filtering, imagetransformations, histograms, structural analysis, shape analysis, objecttracking, motion analysis, feature detection, object detection,stitching, thresholding, image adjustments, etc.), other signalprocessing operations, other image processing operations, visualizing,and/or any other suitable processing operations.

Additionally or alternatively, components of the system 200 and/orsuitable portions of the method 100 can apply artificial intelligenceapproaches (e.g., machine learning approaches, etc.) including any oneor more of: supervised learning (e.g., using logistic regression, usingback propagation neural networks, using random forests, decision trees,etc.), unsupervised learning (e.g., using an Apriori algorithm, usingK-means clustering), semi-supervised learning, a deep learning algorithm(e.g., neural networks, a restricted Boltzmann machine, a deep beliefnetwork method, a convolutional neural network method, a recurrentneural network method, stacked auto-encoder method, etc.) reinforcementlearning (e.g., using a Q-learning algorithm, using temporal differencelearning), a regression algorithm (e.g., ordinary least squares,logistic regression, stepwise regression, multivariate adaptiveregression splines, locally estimated scatterplot smoothing, etc.), aninstance-based method (e.g., k-nearest neighbor, learning vectorquantization, self-organizing map, etc.), a regularization method (e.g.,ridge regression, least absolute shrinkage and selection operator,elastic net, etc.), a decision tree learning method (e.g.,classification and regression tree, iterative dichotomiser 3, C4.5,chi-squared automatic interaction detection, decision stump, randomforest, multivariate adaptive regression splines, gradient boostingmachines, etc.), a Bayesian method (e.g., naïve Bayes, averagedone-dependence estimators, Bayesian belief network, etc.), a kernelmethod (e.g., a support vector machine, a radial basis function, alinear discriminate analysis, etc.), a clustering method (e.g., k-meansclustering, expectation maximization, etc.), an associated rule learningalgorithm (e.g., an Apriori algorithm, an Eclat algorithm, etc.), anartificial neural network model (e.g., a Perceptron method, aback-propagation method, a Hopfield network method, a self-organizingmap method, a learning vector quantization method, etc.), adimensionality reduction method (e.g., principal component analysis,partial lest squares regression, Sammon mapping, multidimensionalscaling, projection pursuit, etc.), an ensemble method (e.g., boosting,boostrapped aggregation, AdaBoost, stacked generalization, gradientboosting machine method, random forest method, etc.), and/or anysuitable artificial intelligence approach.

Embodiments of the system and/or method 100 can include everycombination and permutation of the various system components and thevarious method processes, where one or more instances of the method 100and/or processes described herein can be performed asynchronously (e.g.,sequentially), concurrently (e.g., in parallel), or in any othersuitable order by and/or using one or more instances of the systems,elements, and/or entities described herein.

Any of the variants described herein (e.g., embodiments, variations,examples, specific examples, illustrations, etc.) and/or any portion ofthe variants described herein can be additionally or alternativelycombined, excluded, and/or otherwise applied.

The system and method 100 and embodiments thereof can be embodied and/orimplemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions are preferably executed by computer-executable componentspreferably integrated with the system. The computer-readable medium canbe stored on any suitable computer-readable media such as RAMs, ROMs,flash memory, EEPROMs, optical devices (CD or DVD), hard drives, floppydrives, or any suitable device. The computer-executable component ispreferably a general or application specific processor, but any suitablededicated hardware or hardware/firmware combination device canalternatively or additionally execute the instructions.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A computer-implemented vehicle routing method comprising:routing a vehicle along a route that defines coordinates of travel in athree-dimensional airspace; determining a region on the route that thevehicle is to travel through; while the vehicle is traveling along theroute, detecting an event associated with the region based on anexternal signal; determining that the event is an event of interestbased on one or more parameters associated with the vehicle; andre-routing the vehicle toward the event of interest along a shortenedroute that defines adjusted coordinates of travel in thethree-dimensional airspace, including dynamically modifying thelocomotion component operation to move the vehicle toward the event ofinterest along the shortened route.
 2. The method of claim 1, whereindetermining the region comprises aggregating a set of geographic regionsinto an aggregate region based on overlapping geographic regionsassociated with different vehicle routes.
 3. The method of claim 2,wherein dynamically modifying the locomotion component operationcomprises dynamically modifying one or more of: a vehicle propulsioncomponent or a vehicle steering component to re-route the vehicletowards the event.
 4. The method of claim 3, wherein dynamicallymodifying the locomotion component operation comprises: determiningcontrol instructions for the vehicle based on the shortened route; andsending the control instructions to the vehicle; and wherein the methodfurther comprises monitoring for additional events during vehicle travelalong the shortened route.
 5. The method of claim 1, wherein detectingthe event associated with the region based on the external signalcomprises: collecting a plurality of posts from one or more socialnetworking systems; assigning a subset of posts from the plurality ofposts to the region; determining a keyword frequency for the subset ofposts; and detecting the event associated with the region in response tothe keyword frequency exceeding a historic keyword frequency for theregion.
 6. The method of claim 1, wherein identifying the event furthercomprises identifying a ground-based event based on a vehicle parameterreceived from an airborne Unmanned Aerial Vehicle (UAV).
 7. The methodof claim 6, wherein routing the vehicle along the vehicle routecomprises routing the UAV along a series of waypoints; and whereinre-routing the vehicle along the shortened route comprises re-routingthe UAV along a second series of waypoints between a location and theevent.
 8. A computer-implemented vehicle routing method comprising:routing a vehicle along a route that defines coordinates of travel in athree-dimensional airspace; determining a region on the route that thevehicle is to travel through; while the vehicle is traveling along theroute, detecting an event associated with the region based on anexternal signal; determining an estimated time of arrival (ETA) to theregion based on the route; determining that the event is an event ofinterest based on one or more parameters associated with the vehicle;determining a re-route to the region associated with the event, there-route being determined based on a new route to arrive at the regionprior to the ETA; and re-routing the vehicle toward the event ofinterest according to adjusted coordinates of travel in thethree-dimensional airspace, including dynamically modifying thelocomotion component operation to allow the vehicle to arrive at theevent of interest prior to the ETA.
 9. The method of claim 8, furthercomprising determining one or more new geographic regions for eventmonitoring based on the vehicle re-route.
 10. The method of claim 9,wherein determining the one or more new geographic regions is based onthe vehicle route of an Unmanned Aerial Vehicle (UAV).
 11. The method ofclaim 8, wherein detecting the event associated with the region based onthe external signal comprises: collecting a plurality of data elementsfrom one or more sources of a same source type; assigning a subset ofthe data elements to the region; determining a plurality ofcharacteristics of the subset of the data elements determining afrequency for at least some of the characteristics; and detecting theevent associated with the region in response to the characteristicfrequency exceeding a historic characteristic frequency for the region.12. The method of claim 11, wherein collecting the plurality of dataelements from one or more sources of a same source type furthercomprises: collecting a set of posts from a set of social networkingsystems; and detecting a set of events associated with the geographicregion based on the set of posts, including: assigning a subset of postsfrom the set of posts to the geographic region; determining a keywordfrequency for the subset of posts; and detecting the event for thegeographic region in response to the keyword frequency exceeding ahistoric keyword frequency for the geographic region.
 13. The method ofclaim 11, wherein collecting the plurality of data elements from one ormore sources of a same source type further comprises collecting sensordata associated with the geographic region.
 14. The method of claim 13,further comprising detecting an event from the sensor data associatedwith the geographic region including determining that an event categoryprobability for the event exceeds a category probability threshold. 15.The method of claim 8, further comprising determining the vehicle routebased on an initial event classified as an initial event of interest tothe vehicle.
 16. The method of claim 8, wherein determining the eventparameters for the set of events comprises, for each event of the set ofevents: determining a category probability for an event category basedon signals included in the external signals, the signals associated withthe event; and in response to the category probability exceeding acategory probability threshold, categorizing the event with the eventcategory; and wherein identifying the event comprises identifying theevent based on the event category and the vehicle parameter associatedwith the vehicle.
 17. The method of claim 8, further comprisingassociating the detected event with a second vehicle based on a secondvehicle route for the second vehicle.
 18. The method of claim 8, whereinremotely controlling the locomotion component further comprises remotelycontrolling one or more of a vehicle propulsion component or a vehiclesteering component to re-route the vehicle towards the event.
 19. Themethod of claim 8, wherein re-routing the vehicle toward the event ofinterest according to the adjusted coordinates further comprisesmodifying at least one vertical coordinate for the vehicle.
 20. Acomputer-implemented vehicle routing method comprising: routing avehicle along a route that defines coordinates of travel in athree-dimensional airspace; while the vehicle is traveling along theroute, detecting an event based on an external signal includingdetecting coordinates of the event; determining that the event is anevent of interest based on one or more parameters associated with thevehicle; and re-routing the vehicle toward the coordinates of the eventof interest along a shortened route that defines adjusted coordinates oftravel in the three-dimensional airspace, including dynamicallymodifying the locomotion component operation to depart from the route tomove the vehicle toward the coordinates of the event of interest alongthe shortened route.